关键词: 光伏组件, 可见故障, 树莓派, 内网穿透, 分水岭算法

Abstract: Limited by the complexity and analysis efficiency of image processing, traditional photovoltaic fault diagnosis methods based on image recognition are difficult to achieve real-time monitoring and large-scale fault classification and location. To address this problem, a photovoltaic detection algorithm based on shape factor and improved watershed segmentation was proposed. Firstly, a photovoltaic module segmentation algorithm was designed based on shape factor. The shape factor was defined as the ratio of the area of the connected region to the perimeter. It has scale and rotation invariance, and can extract the contours of photovoltaic modules of different scales in complex backgrounds to avoid interference of background areas on fault diagnosis. Secondly, the watershed algorithm was improved by iterative H value. The over-segmentation phenomenon was suppressed by adjusting the local minimum value, and the fault classification and precise location of the segmented photovoltaic module image were performed. Finally, in order to achieve remote control, the human-computer interaction interface designed by Qt Designer software was embedded in the Raspberry Pi, and the intranet penetration and virtual network console (VNC) were configured. The drone was equipped with a raspberry pi and a high-definition camera to achieve real-time monitoring and fault diagnosis of the photovoltaic station during flight. The experimental results show that the comprehensive accuracy of the proposed algorithm for identifying photovoltaic faults is 85.19%, which is 9.38% higher than the traditional watershed algorithm and the over-segmentation rate is reduced by 28%, indicating that the algorithm can control the over-segmentation phenomenon more effectively and improve the accuracy of fault diagnosis.

Key words: photovoltaic module, visible fault, Raspberry Pi, intranet penetration, watershed algorithm